1. Analyzing Supply Chain Performance under Different Collaborative Replenishment Strategies AIT Masters Theses Competition Wijitra Naowapadiwat Industrial Systems Engineering Asian Institute of Technology May 18, 2009

2. Outline  Problem Statement  Collaborative Replenishment Operation Flow  Model Development  Results Analysis  Conclusion and Future Work

3. Problem Statement Fulfill the requirements between CPFR guideline and supply chain collaboration practice Study different collaborative replenishment strategies and address mechanism analysis Learn impacts of three strategies to the system performance CPFR® Model (VICS Published 2004) Collaborative Planning, Forecasting and Replenishment ( CPFR) is guideline that helps the collaborations between supply chain partners

4. Contribution of this thesis  Develops the sequential diagram which represents the operation flow of collaborative replenishment  Gives an analysis mechanism to approach different collaborative levels  Shows the impact of three strategies to the system performances and provides a better understanding of the supply chain collaboration

5. 1. Develop Time Sequencing Diagram to address operation tasks >> HQ replenishment collaboration >> DC replenishment collaboration >> Store replenishment collaboration 2.Identify Analysis an Input and Expected Output data 3. Develop of the Simulation Model 4. Run the experiment on ARENA under the considered conditions 5.Analyze the impact of Supply chain performance under three Replenishment collaboration strategies on ANOVA 6.Conclusions and Recommendations for future study Methodology Simulation Model Process flow Sequential diagram

6. Sequential diagrams for collaborative replenishment HQ_CR DC_CR Store_CR Three levels of replenishment collaboration between seller and buyer are demonstrated on main collaborative tasks including: Sale forecasting, order forecast, and order generation.

7. Supply Chain Performances  Customer Service Back Order and Fill rate Shortage Rate  Total Supply Chain Cost Inventory Cost + Transportation Cost CarryingShortage

8. Experiment Design  Experiment 1. The impact of supply chain performances under different CR  Experiment 2. The impact of supply chain performances under economic transportation consideration  Independent Variables Supply Capacity ( Cap) Lead time (LT) Demand Variation ( DV)

9. Parameters and Variables design Independent factor Level 123 CRHQDCSt CAPμ*bμ*bμ*aμ*aμ* DV024 LT1 days1 week4 weeks Note * : a>b>1 and μ is averaging of Customer demand

10. General Parameters and Basic assumptions  Number of product and supplier (single product item, 1 supplier)  Number of retailer store ( 1 retailer,2 DCs, 4 stores)  Sale Forecast method (Exponential Moving Average, α= 0.2 )  Inventory Policy ( Periodic review weekly, Up-to-level)  Simulation run length ( 2800 days, 400 wk 30 replications)

11. Supply Network

12. Lead time vs. Impact of supply chain performances under three CR Strategies

13. Demand Variation vs. Impact of supply chain performances under three CR Strategies

14. Supply Capacity vs. Impact of Supply chain performances under three CR Strategies

15. Summary  Level of valued information sharing during order replenishment shows significant impact for supply chain performance  Over all Store CR shows lower total supply chain cost and higher order fulfill rate from simulation results  When limited supply capacity and short lead time, the practitioner may better investment for DC_ CR  Demand uncertainty is high, Store CR is a better choice  Regional DC is needed for a cluster Store replenishment when economic of transportation influences

16. Future Work  Complexity with replenishment collaboration such as multiple manufacturers collaboration for multiple items  Collaborative Forecasting in supply chain network  IT in Order collaborative replenishment

17. Q&A THANK YOU